FF-STGCN:一种基于使用模式相似性的双网络,用于预测自行车共享需求.
Di Yang1,2,3, Ruixue Wu1,2, Peng Wang1,2,3
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
PloS one
|March 7, 2024
概括
准确的共享自行车需求预测对于有效的自行车再平衡和站点规划至关重要. 该FF-STGCN模型通过整合站际流量和类似的使用模式来增强预测,改善自行车的可用性.
科学领域:
- 运输科学 运输科学
- 数据科学数据科学数据科学
- 城市规划 城市规划
背景情况:
- 自行车共享系统在需求预测方面面临挑战,原因是复杂的时空用户行为.
- 不平衡的自行车分布源于用户的任意选择,影响系统的效率.
- 准确的预测对于自行车分配,再平衡和战略站规划至关重要.
研究的目的:
- 提出一个新的双网络模型,FF-STGCN,用于准确预测共享单车需求.
- 在预测模型中有效地整合站际流动和类似的使用模式特征.
- 解决多尺度时空精度的局限性,以改善共享自行车管理.
主要方法:
- 开发了一个多尺度的时空特征融合模块,以提高准确性.
- 构建了一个自行车使用模式相似性学习模块,以捕获站点相关性.
- 采用双网络结构,整合流量和模式特征,用于最终需求预测.
主要成果:
- FF-STGCN模型在Citi Bike数据集上表现出显著的有效性.
- 废弃实验证实了拟议模型中每个模块的关键贡献.
- 该模型成功地整合了各种功能,以便更准确地预测共享自行车需求.
结论:
- 拟议的FF-STGCN模型为共享自行车需求预测提供了有效的解决方案.
- 整合站间流量和使用模式的相似性,可以显著提高预测准确度.
- 这种方法为优化共享自行车系统运营和规划提供了有价值的工具.
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